Scalable Bayesian Optimization via Focalized Sparse Gaussian Processes

A new framework named GADPN has been proposed to enhance Graph Neural Networks (GNNs) by refining graph topology through low-rank denoising and generalized structural perturbation, addressing issues of noise and missing links in graph-structured data.

A new framework for Gaussian Process (GP) model selection, titled 'The Kernel Manifold', has been introduced, emphasizing a geometric approach to optimize the choice of covariance kernels. This method utilizes a Bayesian optimization framework based on kernel-of-kernels geometry, allowing for efficient exploration of kernel space through expected divergence-based distances.

A new framework for transfer learning of discount curves across various fixed-income product classes has been proposed, addressing challenges in estimating these curves from sparse or noisy data. The approach extends kernel ridge regression to a vector-valued setting, leading to a convex optimization problem that promotes smoothness in spread curves between product classes.